For each model, we consider \(p=50\)  \((n=100)\)  and \(p=100\)  \((n=200)\) . We generate random sample sizes of \(n\)  from \(p\) -dimensional Gaussian distribution with mean \(\mathbf {0}_p\)  and covariance in each model and run 100 replications for each model. We measure the performance of the estimator by specificity (sp), sensitivity (se), root mean square error (rmse), \(||\hat{\Sigma }-\Sigma ||_{\operatorname{F}}/p\) , max norm (mnorm), \(||\hat{\Sigma }-\Sigma ||_{\infty }\) , and spectral norm (2norm), \(||\hat{\Sigma }-\Sigma ||_2\) . Specificity and sensitivity denote the ratio of correctly estimated non-zero off-diagonal entries over total non-zero off-diagonal entries in the estimated covariance matrix and the ratio of correctly estimated zero off-diagonal entries over total zero off-diagonal entries in the estimated covariance matrix, respectively. We claim that the estimated off-diagonal entry is 0 if its absolute value does not exceed \(0.001\)  following [1]} and [2]}. For each measure, we provide the mean and the standard deviation over 100 replications.
